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<p>As modern computing systems grow increasingly complex, the efficient management of containerized workloads—such as batch jobs and microservices—has become a critical challenge. Kubernetes has established itself as the de facto container orchestration platform, providing robust and extensible scheduling capabilities. The increasing diversity of computational workloads, each with unique resource requirements, combined with heterogeneous execution environments such as cloud, edge, and fog, has driven research toward adaptive scheduling strategies aimed at enhancing performance and cost efficiency. This article presents a comprehensive literature review of recent advances in Kubernetes scheduling, organized along multiple dimensions: system environments (cloud, edge, fog), workload types (batch, microservices, workflows), optimization objectives (e.g., resource efficiency, Quality of Service (QoS), energy and cost-awareness), scheduling phases, and decision inputs. Rather than imposing rigid categorizations, we highlight how many approaches adopt hybrid strategies that address overlapping concerns. Furthermore, we identify prevailing trends, research gaps, and open challenges, including dynamic adaptability, multi-objective trade-offs, and integration complexity. By providing a structured overview of the state of the art, this review offers researchers and practitioners a clear foundation for guiding future contributions in Kubernetes scheduling research.</p>

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A Survey of Adaptive Scheduling Techniques, Goals, and Challenges in Kubernetes

  • 期刊论文

  • 发表日期：

    2026年1月12日

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  • Said El Kafhali

摘要

As modern computing systems grow increasingly complex, the efficient management of containerized workloads—such as batch jobs and microservices—has become a critical challenge. Kubernetes has established itself as the de facto container orchestration platform, providing robust and extensible scheduling capabilities. The increasing diversity of computational workloads, each with unique resource requirements, combined with heterogeneous execution environments such as cloud, edge, and fog, has driven research toward adaptive scheduling strategies aimed at enhancing performance and cost efficiency. This article presents a comprehensive literature review of recent advances in Kubernetes scheduling, organized along multiple dimensions: system environments (cloud, edge, fog), workload types (batch, microservices, workflows), optimization objectives (e.g., resource efficiency, Quality of Service (QoS), energy and cost-awareness), scheduling phases, and decision inputs. Rather than imposing rigid categorizations, we highlight how many approaches adopt hybrid strategies that address overlapping concerns. Furthermore, we identify prevailing trends, research gaps, and open challenges, including dynamic adaptability, multi-objective trade-offs, and integration complexity. By providing a structured overview of the state of the art, this review offers researchers and practitioners a clear foundation for guiding future contributions in Kubernetes scheduling research.